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Einoshin Suzuki

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2009
58EEEinoshin Suzuki: Negative Encoding Length as a Subjective Interestingness Measure for Groups of Rules. PAKDD 2009: 220-231
57EEEinoshin Suzuki: Discovering Action Rules That Are Highly Achievable from Massive Data. PAKDD 2009: 713-722
2008
56 Régis Gras, Einoshin Suzuki, Fabrice Guillet, Filippo Spagnolo: Statistical Implicative Analysis, Theory and Applications Springer 2008
55 Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi: Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings Springer 2008
54EEShin Ando, Einoshin Suzuki: Unsupervised Cross-Domain Learning by Interaction Information Co-clustering. ICDM 2008: 13-22
53EEEinoshin Suzuki: Pitfalls for Categorizations of Objective Interestingness Measures for Rule Discovery. Statistical Implicative Analysis 2008: 383-395
2007
52 Vincent Corruble, Masayuki Takeda, Einoshin Suzuki: Discovery Science, 10th International Conference, DS 2007, Sendai, Japan, October 1-4, 2007, Proceedings Springer 2007
51 Einoshin Suzuki: Peut-on Capturer la Sémantique à Travers la Syntaxe ? - Découverte des Règles d'Exception Simultanée. EGC 2007: 1
50 Régis Gras, Pascale Kuntz, Einoshin Suzuki: Une règle d'exception en Analyse Statistique Implicative. EGC 2007: 87-98
49EEMarie Agier, Jean-Marc Petit, Einoshin Suzuki: Unifying Framework for Rule Semantics: Application to Gene Expression Data. Fundam. Inform. 78(4): 543-559 (2007)
48EEMasatoshi Jumi, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi, Einoshin Suzuki: Spiral Removal of Exceptional Patients for Mining Chronic Hepatitis Data. New Generation Comput. 25(3): 223-234 (2007)
2006
47EEYukihiro Nakamura, Shin Ando, Kenji Aoki, Hiroyuki Mano, Einoshin Suzuki: Strategy Diagram for Identifying Play Strategies in Multi-view Soccer Video Data. Discovery Science 2006: 173-184
46 Jérôme Maloberti, Shin Ando, Einoshin Suzuki: Classification non-supervisée de données relationnelles. EGC 2006: 389-390
45EEShin Ando, Einoshin Suzuki: An Information Theoretic Approach to Detection of Minority Subsets in Database. ICDM 2006: 11-20
44EENicolas Durand, Bruno Crémilleux, Einoshin Suzuki: Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery. ISMIS 2006: 47-57
43EEEinoshin Suzuki, Shin Ando, Masayuki Hirose, Masatoshi Jumi: Intuitive Display for Search Engines Toward Fast Detection of Peculiar WWW Pages. WImBI 2006: 341-352
42EEEinoshin Suzuki: Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table. J. UCS 12(6): 627-653 (2006)
2005
41EEShin Ando, Einoshin Suzuki, Shigenobu Kobayashi: Sample based crowding method for multimodal optimization in continuous domain. Congress on Evolutionary Computation 2005: 1867-1874
40EEMasanori Yoshinaga, Yukihiro Nakamura, Einoshin Suzuki: Mini-Car-Soccer as a testbed for granular computing. GrC 2005: 92-97
39EEMasatoshi Jumi, Einoshin Suzuki, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi: Multi-strategy Instance Selection in Mining Chronic Hepatitis Data. ISMIS 2005: 475-484
38EEMarie Agier, Jean-Marc Petit, Einoshin Suzuki: Towards Ad-Hoc Rule Semantics for Gene Expression Data. ISMIS 2005: 494-503
37EEEinoshin Suzuki: Worst Case and a Distribution-Based Case Analyses of Sampling for Rule Discovery Based on Generality and Accuracy. Appl. Intell. 22(1): 29-36 (2005)
36EEEinoshin Suzuki, Jan M. Zytkow: Unified algorithm for undirected discovery of exception rules. Int. J. Intell. Syst. 20(7): 673-691 (2005)
2004
35 Einoshin Suzuki, Setsuo Arikawa: Discovery Science, 7th International Conference, DS 2004, Padova, Italy, October 2-5, 2004, Proceedings Springer 2004
34EEMasayuki Hirose, Einoshin Suzuki: Using WWW-Distribution of Words in Detecting Peculiar Web Pages. Discovery Science 2004: 355-362
33EEJérôme Maloberti, Einoshin Suzuki: An Efficient Algorithm for Reducing Clauses Based on Constraint Satisfaction Techniques. ILP 2004: 234-251
32EEEinoshin Suzuki: Undirected Exception Rule Discovery as Local Pattern Detection. Local Pattern Detection 2004: 207-216
2003
31 Ning Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki: Foundations of Intelligent Systems, 14th International Symposium, ISMIS 2003, Maebashi City, Japan, October 28-31, 2003, Proceedings Springer 2003
30EEYuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi: Experimental Evaluation of Time-Series Decision Tree. Active Mining 2003: 190-209
29EEJérôme Maloberti, Einoshin Suzuki: Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates. Discovery Science 2003: 220-232
28EEEinoshin Suzuki, Takeshi Watanabe, Hideto Yokoi, Katsuhiko Takabayashi: Detecting Interesting Exceptions from Medical Test Data with Visual Summarization. ICDM 2003: 315-322
27 Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi: Decision-tree Induction from Time-series Data Based on a Standard-example Split Test. ICML 2003: 840-847
26EEMasaki Narahashi, Einoshin Suzuki: Detecting Hostile Accesses through Incremental Subspace Clustering. Web Intelligence 2003: 337-343
2002
25EEMasaki Narahashi, Einoshin Suzuki: Subspace Clustering Based on Compressibility. Discovery Science 2002: 435-440
24 Fumio Takechi, Einoshin Suzuki: Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction. ICML 2002: 618-625
23EEShutaro Inatani, Einoshin Suzuki: Data Squashing for Speeding Up Boosting-Based Outlier Detection. ISMIS 2002: 601-612
22EEYuta Choki, Einoshin Suzuki: Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance. PKDD 2002: 86-98
21EEEinoshin Suzuki: In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules. Progress in Discovery Science 2002: 504-517
20EEEinoshin Suzuki: Undirected Discovery of Interesting Exception Rules. IJPRAI 16(8): 1065-1086 (2002)
2001
19EEEinoshin Suzuki: Worst-Case Analysis of Rule Discovery. Discovery Science 2001: 365-377
18EEEinoshin Suzuki, Masafumi Gotoh, Yuta Choki: Bloomy Decision Tree for Multi-objective Classification. PKDD 2001: 436-447
2000
17EEEinoshin Suzuki: Issues in Organizing a Successful Knowledge Discovery Contest. Discovery Science 2000: 282-284
16 Einoshin Suzuki, Shusaku Tsumoto: Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets. PAKDD 2000: 208-211
15 Farhad Hussain, Huan Liu, Einoshin Suzuki, Hongjun Lu: Exception Rule Mining with a Relative Interestingness Measure. PAKDD 2000: 86-97
14EEEinoshin Suzuki, Jan M. Zytkow: Unified Algorithm for Undirected Discovery of Execption Rules. PKDD 2000: 169-180
13EEDavid Ramamonjisoa, Einoshin Suzuki, Issam A. Hamid: Research Topics Discovery from WWW by Keywords Association Rules. Rough Sets and Current Trends in Computing 2000: 412-419
1999
12EEEinoshin Suzuki: Scheduled Discovery of Exception Rules. Discovery Science 1999: 184-195
11EEShinsuke Sugaya, Einoshin Suzuki: Normal Form Transformation for Object Recognition Based on Support Vector Machines. Discovery Science 1999: 306-315
10EEEinoshin Suzuki, Toru Ohno: Prediction Rule Discovery Based on Dynamic Bias Selection. PAKDD 1999: 504-508
9 Shinsuke Sugaya, Einoshin Suzuki, Shusaku Tsumoto: Support Vector Machines for Knowledge Discovery. PKDD 1999: 561-567
8 Einoshin Suzuki, Hiroki Ishihara: Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density. RSFDGrC 1999: 414-422
1998
7 Einoshin Suzuki: Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases. KDD 1998: 339-343
6 Einoshin Suzuki, Yves Kodratoff: Discovery of Surprising Exception Rules Based on Intensity of Implication. PKDD 1998: 10-18
1997
5 Einoshin Suzuki: Autonomous Discovery of Reliable Exception Rules. KDD 1997: 259-262
1996
4 Einoshin Suzuki, Masamichi Shimura: Exceptional Knowledge Discovery in Databases Based on Information Theory. KDD 1996: 275-278
1994
3EEPierre Morizet-Mahoudeaux, Einoshin Suzuki, Setsuo Ohsuga: Knowledge-Based Handling of Design Expertise. ICDE 1994: 368-374
1993
2EEEinoshin Suzuki, Tatsuya Akutsu, Setsuo Ohsuga: Knowledge-based system for computer-aided drug design. Knowl.-Based Syst. 6(2): 114-126 (1993)
1991
1EETatsuya Akutsu, Einoshin Suzuki, Setsuo Ohsuga: Logic-based approach to expert systems in chemistry. Knowl.-Based Syst. 4(2): 103-116 (1991)

Coauthor Index

1Marie Agier [38] [49]
2Tatsuya Akutsu [1] [2]
3Shin Ando [41] [43] [45] [46] [47] [54]
4Kenji Aoki [47]
5Setsuo Arikawa [35]
6Yuta Choki [18] [22]
7Vincent Corruble [52]
8Bruno Crémilleux [44]
9Nicolas Durand [44]
10Masafumi Gotoh [18]
11Régis Gras [50] [56]
12Fabrice Guillet [56]
13Issam A. Hamid [13]
14Masayuki Hirose [34] [43]
15Farhad Hussain [15]
16Shutaro Inatani [23]
17Akihiro Inokuchi [55]
18Hiroki Ishihara [8]
19Masatoshi Jumi [39] [43] [48]
20Shigenobu Kobayashi [41]
21Yves Kodratoff [6]
22Pascale Kuntz [50]
23Huan Liu [15]
24Hongjun Lu [15]
25Jérôme Maloberti [29] [33] [46]
26Hiroyuki Mano [47]
27Pierre Morizet-Mahoudeaux [3]
28Yukihiro Nakamura [40] [47]
29Masaki Narahashi [25] [26]
30Toru Ohno [10]
31Muneaki Ohshima [39] [48]
32Setsuo Ohsuga [1] [2] [3]
33Jean-Marc Petit [38] [49]
34David Ramamonjisoa [13]
35Zbigniew W. Ras [31]
36Masamichi Shimura [4]
37Filippo Spagnolo [56]
38Shinsuke Sugaya [9] [11]
39Katsuhiko Takabayashi [27] [28] [30] [39] [48]
40Fumio Takechi [24]
41Masayuki Takeda [52]
42Kai Ming Ting [55]
43Shusaku Tsumoto [9] [16] [31]
44Takashi Washio [55]
45Takeshi Watanabe [28]
46Yuu Yamada [27] [30]
47Hideto Yokoi [27] [28] [30] [39] [48]
48Masanori Yoshinaga [40]
49Ning Zhong [31] [39] [48]
50Jan M. Zytkow [14] [36]

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)